阿片类药物透过血脑屏障的三维构效研究

目的:建立药物透过血脑屏障的三维构效模型,为药物分子设计提供理论依据。方法与结果:利用比较分子力场分析方法建立了阿片类药物透过血脑屏障的三维定量构效模型,该模型有较高的预测能力,交叉验证系数r²_cv=0.718,相关系数r²=0.978,F₃,7=67.902,标准偏差SE=0.209。结论:根据CoMFA模型系数等势图,解释了该类药物透过血脑屏障的构效关系。     

药物透过血脑屏障的3D-QSPR:H2受体拮抗剂的CoMFA和EVA分析

目的:药物透过血脑屏障是药代动力学的重要过程,H2受体拮抗剂是作用于神经外周的抗溃疡药物,为避免该类药物透过血脑屏障损伤中枢神经,产生毒副作用,指导该类药物的设计与合成.方法和结果:选择了不依赖于实验参数的比较分子力场分析(CoMFA)方法和最近发展的本征值(EVA)方法,建立了有关的三维药代动力学性质(3D-QSPR)模型.CoMFA模型的统计参数为:交叉验证系数r2cv=0.625,相关系数r2=0.893,F3,17=47.270,标准偏差SE=0.254;EVA模型的统计参数为:交叉验证系数r2cv=0.697,相关系数r2=0.922,F3,17=67.766,标准偏差SE=0.203.结论:两种方法都能建立三维定量构效模型,EVA模型有更高的预测能力.     

选择性环氧合酶-2抑制剂的三维定量构效研究

目的：建立环氧合酶-2选择性抑制剂的三维构效关系,设计新型的环氧合酶-2抑制剂。方法和结果：通过44个抑制剂与环氧合酶-2的对接确定分子的叠合模式,利用比较分子力场分析方法建立了44个选择性环氧合酶-2抑制剂的三维定量构效模型。模型的交叉验证系数R_CV²=0.709,传统相关系数R_CV²＝0.911,F_5,38＝75.66,标准偏差SE＝0.242。结论：利用DOCK和CoMFA相结合的方法提供了分子设计的新途径。     

噻唑烷二酮和芳酮酸类PPARγ激动剂三维定量构效关系研究

目的建立PPARγ激动剂-噻唑烷二酮和芳酮酸类化合物的三维定量构效关系,为设计高活性PPARγ激动剂提供结构信息。方法与结果用比较分子力场分析方法得到噻唑烷二酮和芳酮酸类化合物CoMFA模型,其交叉验证相关系数R²=0.656,非交叉验证相关系数R²=0.982,F_10,37=201.1,绝对误差SE=0.115。结论从CoMFA系数等势图中揭示芳酮酸类化合物较噻唑烷二酮类化合物活性更高的原因,提示芳酮酸类化合物与PPARγ结合时形成了不同于BRL-PPARγ复合物晶体的结合腔。     

新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

噻唑类衍生物二氢乳清酸脱氢酶抑制剂的三维定量构效关系研究

目的 应用三维定量构效关系（3D-QSAR）研究噻唑类衍生物结构的二氢乳清酸脱氢酶抑制活性,为该类药物的设计和筛选提供可靠的理论依据。方法 针对38个以噻唑为基本骨架的二氢乳清酸脱氢酶抑制剂,分别应用分子力场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）2种经典的方法进行了三维定量构效关系（3D-QSAR）研究,建立相关模型,验证模型的预测能力,三维等势图分析噻唑类衍生物结构与活性的关系。结果 CoMFA模型的交叉验证系数q²为0.796,相关系数r²为0.978;CoMSIA模型的q²以及r²分别为0.721和0.976;2种模型对化合物的活性预测与实际值接近;三维等势图可以全面直观的分析化合物结构对其活性的影响。结论 该3D-QSAR模型三维等势图揭示了结构特征与抑制活性的关系,模型具有较好的预测能力和较强的稳定性,为进一步开发研究打下了较好的基础。     

HPLC法同时测定复方三维右旋泛酸钙糖浆中4中维生素的含量

目的 建立高效液相色谱(HPLC)法同时测定复方三维右旋泛酸钙糖浆中维生素B₁、维生素B₂、维生素B₆和烟酰胺的含量。方法 采用外标法进行测定,色谱柱为Thermo Betasil C₁₈ Analytical(4.6 mm×250 mm, 5 μm),流动相为乙腈-5 mmol·L^-1十二烷基硫酸钠（含0.05%甲酸）水溶液梯度洗脱,流速1.0 mL·min^-1,检测波长260 nm,柱温30 ℃。分别测定10批复方三维右旋泛酸钙糖浆。结果 维生素B₁、维生素B₂、维生素B₆、烟酰胺4种成分的线性范围分别为5.76~115.2 μg·mL^-1(r=0.999 9)、1.16~23.20 μg·mL^-1(r=0.999 9)、1.72~34.4 μg·mL^-1(r=0.999 6)和5.76~115.2 μg·mL^-1(r=0.999 9),平均加样回收率为96.2%~98.4%(RSD为2.14%~3.42%)。不同批次复方三维右旋泛酸钙糖浆中维生素B₁、维生素B₂、维生素B₆、烟酰胺含量范围分别为0.133 7~0.155 9、0.027 86~0.030 71、0.039 05~0.047 7、0.138 7~0.148 2 mg·g^-1。结论 该方法为完善复方三维右旋泛酸钙糖浆的质量标准和加强质量控制提供了新的方法和依据。     

1-咪唑基苯并二氮杂类法呢基蛋白转移酶抑制剂的三维定量构效关系研究

目的建立法呢基蛋白转移酶抑制剂的三维定量构效关系,设计高效法呢基蛋白转移酶抑制剂.方法和结果利用比较分子力场分析方法(CoMFA)建立了32个法呢基蛋白转移酶抑制剂的三维定量构效关系,模型的交叉验证相关系数R²_CV=0.602,非交叉验证相关系数R²=0.958,标准偏差SE=0.270,F=124.5.结论此模型对设计和预测高活性结构类型的化合物有一定的可靠性.     

三维全息原子场作用矢量用于四氢-咪唑-苯二氮酮类抗艾滋病药物定量构效关系研究

目的对23个四氢-咪唑-苯二氮酮(TIBO)类抗艾滋病药物分子进行定量构效关系(QSAR)研究。方法采用本实验室新近提出的三维全息原子场作用矢量(3D-HoVAIF)表征TIBO类抗艾滋病药物分子结构。然后运用偏最小二乘回归(partial least square regression，PLS)建立3D-HoVAIF描述符与TIBO类抗艾滋病药物活性之间的QSAR模型。结果用此方法建模的复相关系数(r^2cum)、交互校验复相关系数(q^{2cum)和模型的标准偏差(SD)分别为r2cum=0.824，q2_cum=0.778与SD=0.56，均优于文献值。结论3D-HoVAIF能较好表征TIBO类抗艾滋病药物分子结构信息，因而能建立具有良好稳定性和预测能力的QSAR模型。}     

噻唑-吡唑啉衍生物作为EGFR抑制剂的3D-QSAR和分子对接模型研究

目的 建立EGFR抑制剂结构和活性之间的关系模型,基于对分子活性产生影响的重要结构性因素的信息,设计新的抑制剂分子并预测其活性,为抑制剂分子的设计提供依据。方法 使用Discovery Studio 2019软件进行3D-QSAR的研究以及偏最小二乘的计算;利用Autodock进行分子对接;使用LigPlot研究二维相互作用。结果 模型具有较高的q²(0.521),和r²(r²_training=0.993,r²_test=0.916,r²_blind=0.940),表明模型具有较高的预测能力和拟合能力。结论 预测结果表明,新设计的化合物活性较高,为EGFR抑制剂分子的设计提供了参考。     

3D QSAR studies on cinnamaldehyde analogues as farnesyl protein transferase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on 59 cinnamaldehyde analogues as Farnesyl Protein Transferase (FPTase) inhibitors were investigated using comparative molecular field analysis (CoMFA) with the PLS region-focusing method. Forty-nine training set inhibitors were used for CoMFA with two different grid spacings, 2A and 1A. Ten compounds, which were not used in model generation, were used to validate the CoMFA models. After the PLS analysis, the best predictive CoMFA model showed that the cross-validated value (r2cv) and the non-cross validated conventional value (r2ncv) are 0.557 and 0.950, respectively. From the CoMFA contour maps, the steric and electrostatic properties of cinnamaldehyde analogues can be identified and verified.     

Non-nucleoside inhibitors of NS5B polymerase binding to allosteric sites: 3D- QSAR and molecular docking studies

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. Nonstructural (NS5B) viral proteins have emerged as an attractive target for drug discovery efforts toward antiviral for hepatitis C virus. Toward this target several series of NS5B inhibitors that showed activity in the replicon assay have been reported. In this article, we gave a report of the NS5B allosteric sites and the corresponding non-nucleoside inhibitors, which belong to different chemical classes. Then using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods, 3-dimension quantitative structure-activity relationships (3D-QSAR) models have been built with more than two hundred benzimidazole/indole derivative inhibitors. These studies indicated that the QSAR models were statistically significant and had high predictabilities (CoMFA: q(2)=0.823, r(2)=0.942; CoMSIA: q(2)=0.817, r(2)=0.935). The flexible docking method, which was performed by the DOCK6.0 software, positioned all of the inhibitors into the allosteric site to determine the probable binding conformation. The CoMFA and CoMSIA models based on the docking conformations also yielded statistically significant and high predictive QSAR models (CoMFA: q(2)=0.509, r(2)=0.768; CoMSIA: q(2)=0.582, r(2)=0.854). Our models would offer help to better comprehend the structure-activity relationships existent for this class of compounds and also facilitate the design of new inhibitors with good chemical diversity.     

芳构酶抑制剂7α-取代雄甾二酮三维定量构效关系的研究

目的 建立7α-取代雄甾二酮芳构酶抑制剂的三维定量构效模型，为设计高效芳构酶抑制提供理论依据。方法 利用比较分子力场分析方法，建立了30个7α-取代雄甾二酮芳构酶抑制剂的三维定量构效模型。结果 该模型的交叉验证相关系数Rcv^2＝0．721，非交叉验证相关系数只R^2＝0．994，标准偏差SE＝0．062，F＝170．787。用此模型预测了4个芳构酶抑制剂的活性，结果与实验值相符。结论 该模型有较高的预测能力，可为甾体芳构酶抑制剂的结构优化提供理论指导。     

3D-QSAR studies on chromone derivatives as HIV-1 protease inhibitors: application of molecular field analysis

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for chromone derivatives against HIV-1 protease using molecular field analysis (MFA) with genetic partial least square algorithms (G/PLS). Three different alignment methods: field fit, pharmacophore-based, and receptor-based were used to derive three MFA models. All models produced good predictive ability with high cross-validated r(2) (r(2) (cv)), conventional r(2), and predictive r(2)(r(2)(pred)) values. The receptor-based MFA showed the best statistical results with r(2) (cv) = 0.789, r(2)= 0.886, and r(2)(pred) = 0.995. The result obtained from the receptor-based model was compared with the docking simulation of the most active compound 21 in this chromone series to the binding pocket of HIV-1 protease (PDB entry 1AJX). It was shown that the MFA model related well with the binding structure of the complex and can provide guidelines to design more potent HIV-1 protease inhibitors.     

糖蛋白IIb/IIIa受体拮抗剂三维构效关系研究

目的：研究三维构效关系,建立药效模型,为设计新型糖蛋白IIb/IIIa受体拮抗剂提供指导。方法和结果：利用比较分子力场方法,建立了IIb/IIIa受体拮抗剂的三维定量构效模型。在比较分子力场分析中,利用拮抗剂晶体结构为模板,进行了多种分子叠合形式研究,建立了具有良好预测能力的三维定量构效关系模型,表征模型预测能力的交叉验证系数R_CV²＝0.834,传统相关系数R²＝0.988,F＝323.63,标准偏差SE（Standard Error of Estimate）＝0.135。结论：所得模型解释了已有的构效关系,系数等势图映射的受体性质与实验结果相一致,可以指导新的拮抗剂设计。     

3D-QSAR CoMFA study on imidazolinergic I(2) ligands: a significant model through a combined exploration of structural diversity and methodology

Displaying an unprecedented structural diversity, 119 I(2) ligands, and their pK(i) values, were collected and submitted to a comparative molecular fields analysis (CoMFA) study. They were discerned into three structural subsets (A, B, C), to explore the I(2) 3D-QSARs from finite structural systems (A, B, C) to more complex ones (AB, AC, BC, ABC). In addition, various key steps of the CoMFA methology were explored. The applied method used two pharmacophore templates and seven molecular field combinations (electrostatic, lipophilic, steric), as well as eight alignment methods (two point-by-point and six similarity-based variations). That way, 644 CoMFA models were obtained and further selected according to their predictive ability through two filters. The first filter was mainly based on the q(2), which internally evaluates the predictive ability from the training set. For the second filter, the predictive ability was externally evaluated through the prediction of test sets. Finally, one model was extracted from the whole data as the best. Indeed, it combines three features of upmost importance for the further design of ligands endowed with high I(2) affinity: structural diversity (n = 73), robustness (N = 9, r(2) = 0.96, s = 0. 28, F = 148), and a great fully assessed predictive ability (q(2) = 0.50, r(2)(test set) = 0.81, n(test set) = 46). On the basis of structural data and CoMFA isocontours, some elements of the I(2) tridimensional pharmacophore are also suggested.